EP3854887A1 - In vitro verfahren zur identifizierung effizienter therapeutischer molekule zur behandlung des pankreatischen duktalen adenokarzinoms - Google Patents

In vitro verfahren zur identifizierung effizienter therapeutischer molekule zur behandlung des pankreatischen duktalen adenokarzinoms Download PDF

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EP3854887A1
EP3854887A1 EP20305052.1A EP20305052A EP3854887A1 EP 3854887 A1 EP3854887 A1 EP 3854887A1 EP 20305052 A EP20305052 A EP 20305052A EP 3854887 A1 EP3854887 A1 EP 3854887A1
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Prior art keywords
sensitivity
candidate
model
tumor samples
tumor
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French (fr)
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Olivier TURRINI
Marine GILABERT
Marc Giovannini
Rémy Nicolle
Yuna BLUM
Juan Iovanna
Nelson Dusetti
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Ligue Nationale Contre Le Cancer
Aix Marseille Universite
Centre National de la Recherche Scientifique CNRS
Institut National de la Sante et de la Recherche Medicale INSERM
Institut Jean Paoli and Irene Calmettes
Original Assignee
Ligue Nationale Contre Le Cancer
Aix Marseille Universite
Centre National de la Recherche Scientifique CNRS
Institut National de la Sante et de la Recherche Medicale INSERM
Institut Jean Paoli and Irene Calmettes
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Priority to EP20305052.1A priority Critical patent/EP3854887A1/de
Priority to PCT/EP2021/051395 priority patent/WO2021148573A1/en
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57438—Specifically defined cancers of liver, pancreas or kidney
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2570/00—Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention relates to an in vitro method allowing the identification of efficient therapeutic molecules for treating pancreatic ductal adenocarcinoma method, in particular to an in vitro method for determining a transcriptomic signature of sensitivity of pancreatic ductal adenocarcinoma (PDAC) tumor sample of human or animal origin to a therapeutic candidate molecule, and to an in vitro method for determining the sensitivity of PDAC cancer cells of a patient to a target molecule.
  • PDAC pancreatic ductal adenocarcinoma
  • the present invention also relates methods and algorithms suitable for use to evaluate the efficiency of an anticancer compounds for each PDAC patients or to select the most efficient anticancer compound for each PDAC patient.
  • the present invention relates generally to the field of transcriptomics, for effective treatment of individuals with a Pancreatic Ductal Adenocarcinoma (PDAC).
  • PDAC Pancreatic Ductal Adenocarcinoma
  • brackets ( [ ] ) refer to the List of References provided at the end of the document.
  • Pancreatic Ductal Adenocarcinoma is one of the most aggressive gastrointestinal tumors.
  • An earliest genetic event in the progression of the normal ductal epithelia to premalignant pancreatic intraepithelial neoplasia (PanIN) is the mutation of the K-Ras oncogene ( Deramaudt T, Rustgi AK, Mutant KRAS in the initiation of pancreatic cancer, Biochim Biophys Acta. 2005 Nov 25; 1756(2):97-101 [1]).
  • the mutational activation of KRAS protein triggers diverse downstream effector proteins that sustain proliferation, metabolic reprogramming, anti-apoptosis, evasion of the immune response and remodeling of the microenvironment.
  • Tumor mass contains also cells such as the cancer-associated fibroblasts (CAFs), the T cells, the stellate cells, the macrophages, the regulatory T cells, the endothelial cells and others (Ryan Carr et al. 2016 [5] ; and Pillarisetty, 2014 [6] ).
  • CAFs cancer-associated fibroblasts
  • PDAC is a heterogeneous disease with a variable clinical evolution, an inconstant response to the treatments and histopathologically may present some differentiation degrees.
  • survival after the time of diagnosis could be from 3 months to more than 5-6 years or even more
  • efficient response to the gemcitabine treatments is of 10% (Burris et al., 1997 [9] ), whereas to folfirinox is around 30% (Conroy et al., 2011 [10] ), and histologically PDAC tumors could be from very to poorly differentiate.
  • the inventors (Nicolle et al., 2017 [11] ) and others (Bailey et al., 2016 [12] ) have reported that the clinical evolution, the response to the treatments as well as the differentiation degrees cannot be explained by the genetic mutations. On the contrary, the inventors demonstrated that at least the overall survival and histological characteristics of the tumors are determined by the epigenetic landscape including DNA methylation (Nicolle et al., 2017 [11] ) and specific histones marks (Lomberk et al., 2018 [13] ). This is conceptually important since epigenetic, contrary to the majority of the genetic mutation, is druggable and therefore modifiable.
  • Histology is a good indicator to predicting the survival time (Lenz et al., 2011 [14] ) but is it only possible in a small amount as lesser than 15% of the operable patients. While on the non-operable patients, who have a most evolved disease and represent around of 85%, the only available material is a small number of fresh cells, obtained by Endoscopic ultrasound-guided fine needle aspiration biopsy (EUS-FNA), that results not enough for determine its differentiation status. Therefore, to phenotypically characterize all the PDAC became urgent since it could be determinant for the therapeutic approach. In particular, there remains a need for in vitro or ex vivo methods for determining the short-term and long-term survival (prognosis) of said individual; or alternatively for determining the level of differentiation of PDAC in said individual.
  • EUS-FNA Endoscopic ultrasound-guided fine needle aspiration biopsy
  • MSI status is a predictor of response to immunotherapies based on PDL1 inhibitor.
  • Germline BRCA1 and BRCA2 mutations were shown to be predictive of response to PARP inhibitors.
  • dCK, hent1, and other transporters or enzymes implicated in the uptake and metabolism of the gemcitabine have also been shown to be predictive of response to gemcitabine.
  • these results were challenged several times and it was demonstrated that the absence of efficient antibodies to quantify these proteins in situ was a major obstacle.
  • the present invention relates to a method for determining a prediction function configured to generate a sensitivity degree of a tumor sample to a candidate therapeutic molecule, wherein the tumor sample is of human or animal origin, said method comprising:
  • the selected candidate prediction function is preferably a candidate prediction function that minimizes a prediction error.
  • the step of selecting a candidate prediction function among the candidate prediction functions may comprise selecting a candidate prediction function for which the predicted sensibility degrees obtained for the tumor samples of the second batch and the measures of sensitivity obtained for the tumor samples of the second batch of tumor samples are the most correlated.
  • each of the set of at least one supervised or semi-supervised machine learning method is configured to:
  • the set of at least one supervised or semi-supervised machine learning method may comprise a matrix factorization method allowing an identification of at least partially decorrelated matrix components, wherein determining the candidate prediction functions comprises:
  • the step of applying a candidate prediction function to a second reference vector may comprise applying to the concerned second reference vector the projection function on the most predictive component to generate a predicted sensibility degree for the corresponding tumor sample of the second batch of tumor samples.
  • the method may further comprise a step of computing a sensitivity signature to the candidate therapeutic molecule as a distribution of prediction values generated by applying the selected candidate prediction function (SPF) to at least some of the second reference vectors, wherein the orientation of the scale of the prediction values is determined on the basis of the location, on the scale of the prediction values, of the prediction values of the second reference vectors associated with the highest and / or lowest measures of sensitivity.
  • SPF selected candidate prediction function
  • the present invention relates also to a method for determining a sensitivity degree of a tumor sample of a patient to one or more candidate therapeutic molecules, said method comprising the steps of:
  • the method for determining a sensitivity degree of a tumor sample of a patient to one or more candidate therapeutic molecules according to the present invention may for example comprise the steps of:
  • each of at least one supervised or unsupervised machine learning method may be chosen from neural network methods, decision trees, k-nearest neighbors method, carrier vector machines, algorithm based on a linear model, a generalized linear model discriminant, a factor regression model, a partial least square model, a factor analysis, a support vector machine, a support vector regression, a graphical model, a tree-based model, a random forest model, a random ferns model, a naive Bayes model, a linear discriminant analysis, a quadratic linear discriminant analysis, a perceptron model, a neural network model, nearest neighbor model, a nearest prototype model, an ensemble model, a prototype-based supervised algorithm, a bagged model, a Bayesian model, a regularized linear model, a polynomial model, a rule-based model, a Gaussian process model, a mixture discriminant model, a regression spline model, a rule induction method, a prototype model,
  • the first and / or second reference transcriptomic profile(s) of tumor sample of human origin may be obtained from patients-derived tumor xenografts, from patients-derived organoids, from patients-derived cell lines or cell culture or from patients-derived tissue.
  • the cancer may be selected from colorectal cancer, breast cancer, lung cancer, prostate cancer, melanoma, bladder cancer, kidney cancer, neuroendocrine tumors, sarcoma, head and neck cancers, liver cancer, endometrial cancer, gastric cancer, biliary tract cancer, pancreatic ductal adenocarcinoma (PDAC), the tumor sample being a tumor sample of said cancer.
  • colorectal cancer breast cancer, lung cancer, prostate cancer, melanoma, bladder cancer, kidney cancer, neuroendocrine tumors, sarcoma, head and neck cancers, liver cancer, endometrial cancer, gastric cancer, biliary tract cancer, pancreatic ductal adenocarcinoma (PDAC), the tumor sample being a tumor sample of said cancer.
  • PDAC pancreatic ductal adenocarcinoma
  • the cancer may be selected from pancreatic ductal adenocarcinoma (PDAC), wherein the first batch of different tumor samples of human or animal origin is a batch of different PDAC tumor samples of human or animal origin, and wherein the second batch (B2) of different tumor samples of human or animal origin is a batch of different PDAC tumor samples of human or animal origin.
  • PDAC pancreatic ductal adenocarcinoma
  • sample means any tissue tumor sample obtained and/or derived from a patient.
  • Said tissue sample is obtained for the purpose of the in vitro methods of the present invention.
  • the sample can be fresh, frozen, fixed or embedded (e.g., paraffin embedded) or derived (Patients-derived Tumor Xenografts (PDTX)).
  • the tumor sample may result for example from the tumor resected from the patient.
  • the tumor sample may also result from a biopsy performed in the primary tumor of the patient or performed in metastatic sample distant from the primary tumor of the patient.
  • the sample may be obtained for example through endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) biopsy samples from patients with unresectable tumors or from tumor tissue samples from patients undergoing surgery as disclosed in Vilmann P et al. 1992 [15].
  • EUS-FNA endoscopic ultrasound-guided fine-needle aspiration
  • the samples are Patients-derived Tumor Xenografts (PDTX)
  • the samples may be obtained by any protocol known by the skilled person, for example by the protocol disclosed in document Duconseil et al., 2015 [16]. The same applies for a sample for which the sensitivity degree to a therapeutic molecule is to be determined.
  • the samples of the first batch of different PDAC tumour sample of human or animal origin and the samples of the second batch of different PDAC tumour sample of human or animal origin are obtained by the same method.
  • patient means human or animal patient.
  • the first and second reference vectors representing respectively reference transcriptomic profiles of tumor samples of human or animal origin may be obtained by any method known by the skilled person.
  • the methods described below are not specific to any gene expression quantification method and can be applied to any RNA-based quantification of gene expression without any specificity on processing and normalization, which includes but is not limited to: RT-qPCR, NanoString, short read Illumina sequencing often referred to as RNA-Seq, long read sequencing, microarray-technology, etc.
  • RT-qPCR RT-qPCR
  • NanoString NanoString
  • short read Illumina sequencing often referred to as RNA-Seq
  • long read sequencing microarray-technology, etc.
  • the following publications are disclosing such methods:
  • the first and second reference vectors may be obtained by collecting for each sample the level of expression of each measured gene by obtaining the relative quantification of the corresponding mRNAs.
  • Each gene-associated mRNA quantification, a real value, is concatenated to form a vector of real values.
  • the reference transcriptomic profiles of tumor samples of human or animal origin may be obtained from tumor samples, from cell lines, from organoids or from patients-derived tumor xenografts, in particular for screening candidate therapeutic molecules and /or sensitivity degrees of tumor samples from donors or patients, for therapeutic and/or research purposes, in particular for the treatment of PDAC.
  • the present invention relates also to a computer readable medium comprising computer program instructions which when executed causes an apparatus to perform the steps of any of the methods of the present invention as defined and described herein.
  • the present invention relates in particular to computer readable storage medium having computer readable program instructions embodied therein, which when being loaded on a computer, one or more processors and / or a programmable hardware component in an apparatus, cause the apparatus to perform one or more steps of one or more of the methods described herein.
  • a further embodiment may be a computer program product comprising such a computer readable storage medium.
  • the computer readable storage medium may be non-transitory.
  • a computer storage medium may be any physical media that can be read, written or more generally accessed by a computer.
  • Embodiments of a computer-readable medium includes, but are not limited to, both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another.
  • computer readable program instructions to perform embodiments described herein may be stored, temporarily or permanently, in whole or in part, on a non-transitory computer readable medium of a local or remote storage device including one or more storage media.
  • Examples of computer storage media include, but are not limited to, a flash drive or other flash memory devices (e.g. memory keys, memory sticks, USB key drive), CD-ROM or other optical storage, DVD, magnetic disk storage or other magnetic storage devices, solid state memory, memory chip, RAM, ROM, EEPROM, smart cards, a relational database management system, a traditional database, or any other suitable medium from that can be used to carry or store computer program instructions in the form of instructions or data structures which can be read by a computer processor.
  • various forms of computer-readable medium may be used to transmit or carry instructions to a computer, including a router, gateway, server, or other transmission device, wired (coaxial cable, fiber, twisted pair, DSL cable) or wireless (infrared, radio, cellular, microwave).
  • the computer program instructions may include code from any computer-programming language, including, but not limited to, assembly, C, C++, Basic, SQL, MySQL, HTML, PHP, Python, R, Julia, Java, Javascript, etc.
  • the present invention relates also to an apparatus comprising means for performing or means configured for performing the steps of any of the method of the present invention as defined and described herein.
  • Means for performing one or more functions may also comprises one or more processors and one or more memories (e.g. in a system or apparatus) for storing computer program instructions configured to, when executed by at least one processor, cause the performance (e.g. by the system or apparatus) of the one or more functions.
  • processor When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor” may implicitly include, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), etc. Other hardware, conventional or custom, may also be included. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a memory may include a random-access memory (RAM), cache memory, non-volatile memory, backup memory (e.g., programmable or flash memories), read-only memory (ROM), a hard disk drive (HDD), a solid state drive (SSD) or any combination thereof.
  • RAM random-access memory
  • non-volatile memory non-volatile memory
  • backup memory e.g., programmable or flash memories
  • ROM read-only memory
  • HDD hard disk drive
  • SSD solid state drive
  • the ROM may be configured to store, amongst other things, an operating system of the system / apparatus and / or computer program instructions.
  • the RAM may be used by the processor(s) for the temporary storage of data.
  • any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention.
  • any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
  • the inventors have defined the sensitivity to chemotherapeutic molecule of an important number of cell lines, obtained their whole transcriptome, identified a set of putative RNA predictive signatures associated to the sensitivity of cell lines to these molecules by using an approach based a statistical analysis method, for example, based in an Independent Component Analysis (ICA), defined the sensitivity to chemotherapeutic agents of Patients-derived Tumor Xenografts (PDTX), obtained their whole transcriptome, selected an effective signature from the set of putative RNA predictive signatures to obtain an RNA signature predictive of the response to chemotherapeutic molecules in PDAC.
  • ICA Independent Component Analysis
  • the inventors have also identified a group of genes predictive of response to chemotherapeutic molecules to be used as molecular markers.
  • Patient informed consent forms were collected and registered in a central database.
  • Exemple 1 obtaining the sensitivity to target molecules for a first set of tumor samples, cell lines
  • EUS-FNA endoscopic ultrasound-guided fine-needle aspiration
  • each sample obtained from EUS-FNA was mixed with 100 ⁇ L of Matrigel (BD Biosciences, Franklin Lakes, NJ) and was injected in the upper right flank of a nude immunosuppressed nude mice (Swiss Nude Mouse Crl: NU(lco)-Foxnlnu; Charles River Laboratories, Wilmington, MA).
  • a nude immunosuppressed nude mice Swiss Nude Mouse Crl: NU(lco)-Foxnlnu; Charles River Laboratories, Wilmington, MA.
  • Each sample derived from surgery resection was fragmented, mixed with 100 ⁇ L of Matrigel, and implanted with a trocar (10 gauge; Alternative Research of America, Sarasota, FL) in the subcutaneous right upper flank of an anesthetized and disinfected mouse.
  • mice When the tumors reached 1 cm 3 , the mice were sacrificed, and the tumors were removed. Xenografts that failed to develop within 6 months were discontinued. The study on animals was approved by the Animal Facility and Experimental Platform (Scientific and Technological Park of Luminy, Marseille, France).
  • Sensitivity to five chemotherapeutic target molecules - gemcitabine, irinotecan, oxaliplatine, docetaxel and 5FU - was determined by performing dose-response assays.
  • the viability of cell lines was measured after being cultivated for 72h in media with increasing dose of target molecules ranging from 0.01nM to 100 ⁇ M. Quantification of viable cells was performed using Cell survival was quantified using the Cell Titer-Glo assay (Promega Corporation) 3 days after adding chemo to the culture media.
  • Example 2 Obtention of the reference vectors representing respectively reference transcriptomic profiles of the first set of tumor samples
  • RNA-seq As disclosed in Lomberk et al., 2018 [13] and in Nicolle et al., 2017 [11] .
  • RNA expression profiles were obtained using Illumina's TrueSeq Stranded mRNA LT protocol. Sequencing followed oligo-dT capture and was done on a paired-end 100 pair flow cell. RNA libraries were prepared and sequenced by AROS Applied Biotechnology A/S (Aarhus, Denmark). RNA-seq reads were mapped using STAR 18 with the proposed ENCODE parameters, as disclosed in Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner, Bioinformatics, 2013 [21] Jan 1;29(1):15-21.
  • the next step was to identify a set of putative RNA signatures associated to the sensitivity to each target since we demonstrated that the phenotype of the PDAC is controlled by the transcriptome and epigenome (Lomberk et al., 2018 [13] ; Nicolle et al., 2017 [11] ).
  • ICA Independent Component Analysis
  • the growth 12 PDTX as defined above was measured in mouse during treatment with each chemotherapeutic target molecule independently and under a control treatment with DMSO. The growth rate under each chemotherapeutic target molecule was compared with the control to determine the sensitivity.
  • RNA candidate signature predictive of the sensitivity to each chemotherapeutic target molecules was applied to the vectors representing respectively reference transcriptomic profiles of the second set of 12 PDTX tumor samples.
  • the resulting predicted sensitivity of the 12 PDTX to the five chemotherapeutic target molecules was compared to the measured sensitivity.
  • the candidate RNA predictive signature with the predicted sensitivity that was the closest to the observed sensitivity was selected as the final RNA signature predictive of the sensitivity to each chemotherapeutic target molecules.
  • a method for determining a prediction function adapted to generate a sensitivity degree of a tumor sample to a candidate therapeutic molecule an RNA is be described.
  • the tumor sample is of human origin.
  • the prediction function is configured to be applied to a vector representing a transcriptomic profile of a tumor sample to generate a sensitivity degree of the tumor sample with respect to the given candidate therapeutic molecule.
  • the prediction function is determined on the basis of reference transcriptomic profiles generated for two batches B1 and B2 of tumor samples, which may be of human or animal origin.
  • a transcriptomic profile of tumor sample may be obtained from patients-derived tumor xenografts, from patients-derived organoids, from patients-derived cell lines or cell culture or from patients-derived tissue or using any appropriate method.
  • the transcriptomic profile is obtained as disclosed in example 2 above.
  • Each transcriptomic profile may be encoded as a data vector representing the gene expression, e.g. according to a RNA analysis of the tumor sample.
  • the gene expression is determined with respect to a given set of genes.
  • Each vector coefficient of a data vector representing a transcriptomic profile is equal to the number of genes obtained for a given gene of the set of genes.
  • a measure of sensitivity to a candidate therapeutic molecule may be obtained for each reference transcriptomic profile corresponding to a tumor sample in any of the two batches B1 and B2 of tumor samples.
  • Example methods for obtaining such measure of sensitivity to a candidate therapeutic molecule are disclosed herein.
  • Figure 1 shows a flowchart of an example method for determining a prediction function. While the steps are described in a sequential manner, the man skilled in the art will appreciate that some steps may be omitted, combined, performed in different order and / or in parallel.
  • Figure 3 shows a data diagram that illustrates the processing steps of the method of Figure 1 .
  • step 310 first reference vectors representing respective first reference transcriptomic profiles of tumor samples of the first batch B1 of tumor samples are obtained.
  • step 320 a measure of sensitivity of the tumor to the candidate therapeutic molecule is obtained for each first reference transcriptomic profile.
  • the tumor sensitivity to gemcitabine for a tumor sample of human origin is obtained from cell line cultures. It may also be obtained by using another source or method described herein or other known methods.
  • D1 i (1 ⁇ i ⁇ N1) be the measure of sensitivity of the tumor to the candidate therapeutic molecule obtained for the transcriptomic profile encoded by the vector V1 i .
  • a correlation analysis is performed to obtain a set of M candidate prediction functions using a set of one or more supervised or semi-supervised machine learning methods.
  • Each of the candidate prediction functions is configured to generate a sensitivity degree from a vector representing a transcriptomic profile.
  • Each of the candidate prediction functions is determined by using the first reference vectors V1 i (1 ⁇ i ⁇ N1) and the corresponding measures of sensitivity D1 i (1 ⁇ i ⁇ N1) as learning data of a supervised or semi-supervised machine learning method in order to determine one or more parameters of the concerned the candidate prediction function.
  • a method of the set of one or more supervised or semi-supervised machine learning methods may be a machine learning method configured to identify a set of gene expression components of the transcriptomic profiles represented by the first reference vectors that are correlated with the measures of sensitivity obtained for the tumor samples of the first batch B1 of tumor samples.
  • One or more candidate prediction functions may be derived from the result of the correlation analysis: one or more parameters of the candidate prediction function are determined based on the mathematical relationship between the identified set of gene expression components and the measures of sensitivity.
  • a method of the set of one or more supervised or semi-supervised machine learning methods may be chosen from neural network methods, decision trees, k-nearest neighbors method, carrier vector machines, algorithm based on a linear model, a generalized linear model discriminant, a factor regression model, a partial least square model, a factor analysis, a support vector machine, a support vector regression, a graphical model, a tree-based model, a random forest model, a random ferns model, a naive Bayes model, a linear discriminant analysis, a quadratic linear discriminant analysis, a perceptron model, a neural network model, nearest neighbor model, a nearest prototype model, an ensemble model, a prototype-based supervised algorithm, a bagged model, a Bayesian model, a regularized linear model, a polynomial model, a rule-based model, a Gaussian process model, a mixture discriminant model, a regression spline model, a rule induction method, a prototype model,
  • the set of one or more supervised or semi-supervised machine learning methods may include only one machine learning method with associated distinct candidate parameter sets and / or distinct machine learning methods with respective one or more candidate parameter sets.
  • the candidate prediction function may be a linear function, encoded by a projection vector and / or a set of coefficients, configured to be applied a vector representing a transcriptomic profile to generate a sensitivity degree.
  • the parameters of the candidate prediction function comprise the coefficients of the projection vector.
  • step 330 one or more parameters are obtained for the each of the candidate prediction functions.
  • step 340 second reference vectors representing respective second reference transcriptomic profiles of tumor samples of the second batch B2 of tumor samples are obtained.
  • N2 be the number of second reference transcriptomic profiles
  • G be the number of genes in the set of genes for which the RNA expression was measured.
  • step 350 a measure of sensitivity of the tumor to the candidate therapeutic molecule is obtained for each second reference transcriptomic profile.
  • the sensitivity to a therapeutic molecule is known or may be determined.
  • D2 i (1 ⁇ i ⁇ N2) be the measure of sensitivity of the tumor to the candidate therapeutic molecule obtained for the transcriptomic profile encoded by the vector V2 i .
  • each candidate prediction function is applied to each of at least one of the second reference vectors V2 i to generate a predicted sensibility degree for the corresponding tumor sample of the second batch B2 of tumor samples.
  • PF f be the f th candidate prediction function of the considered set of candidate prediction functions (1 ⁇ f ⁇ L).
  • PS fi PF j (V2 i ).
  • a candidate prediction function among the L candidate prediction functions is selected on the basis of a comparison of the predicted sensibility degrees PS fi (1 ⁇ i ⁇ N2, 1 ⁇ f ⁇ L) obtained for the tumor samples of the second batch B2 and the measures of sensitivity D2 i (1 ⁇ i ⁇ N2) obtained for the tumor samples of the second batch B2 of tumor samples.
  • the selection of the candidate prediction function may be performed in various ways.
  • the selected candidate prediction function SPF may be a candidate prediction function PF f that minimizes a prediction error.
  • the selected candidate prediction function SPF may be the candidate prediction function PF f that minimizes the sum of differences: ⁇ i PS fi ⁇ D 2 i
  • the selected candidate prediction function SPF obtained is a prediction function specific to a given candidate therapeutic molecule and is adapted to be applied to a vector representing a transcriptomic profile of a tumor sample to generate a sensitivity degree of the tumor sample with respect to a given candidate therapeutic molecule.
  • Figure 2 shows a flowchart of an example method for determining a prediction function using a machine learning method based on a matrix factorization method.
  • Figure 3 shows a data diagram that illustrates the processing steps of the method of figure 1 .
  • a matrix M1 (hereafter the first reference matrix) of vectors is generated from the N1 first reference vectors representing the N1 first reference transcriptomic profiles.
  • step 420 like in step 320, a measure of sensitivity of the tumor to the candidate therapeutic molecule is obtained for each first reference transcriptomic profile.
  • a correlation analysis is performed by applying a machine learning method based on a matrix factorization method allowing an identification of at least partially decorrelated components.
  • a machine learning method based on a matrix factorization method allowing an identification of at least partially decorrelated components.
  • only one machine learning method is used but with distinct candidate parameter sets to generate distinct candidate prediction functions.
  • distinct candidate parameter sets are defined.
  • the matrix factorization method enables to provide a new expression (hereafter the projection matrix P1i) in a new mathematical space (the projection space) of the initial matrix M1 on the basis of components that are at least partially decorrelated compared to the initial mathematical space of gene expression in which the initial matrix M1 is defined.
  • the matrix factorization method may be for example an independent component analysis (ICA) method, a non-negative matrix factorization method, a principal component analysis (PCA) method, a multi-dimensional scaling (MDS) method, a matrix decomposition based on eigenvalues method, a correspondence (CA) analysis method, a latent semantic analysis (LSA) method, a canonical correlation method, a factor analysis method, etc.
  • ICA independent component analysis
  • PCA principal component analysis
  • MDS multi-dimensional scaling
  • CA correspondence
  • LSA latent semantic analysis
  • canonical correlation method a factor analysis method, etc.
  • the matrix M1 may be expressed as M 1 ⁇ Qf * P 1 f
  • Qf is a matrix (hereafter, the coefficient matrix) of size G ⁇ Kf
  • P1f is a matrix (hereafter, the projection matrix) of size Kf ⁇ N1, where Kf is the number of components (hereafter, the ICA components) resulting from the factorization (e.g. the independent component analysis).
  • the projection matrix P1f represents the projection of the N1 vectors on the Kf components resulting from the matrix factorization of the first matrix M1.
  • Decomposing the first reference matrix M1 by the matrix factorization method allows an identification of at least partially decorrelated matrix components, the number of at least partially decorrelated matrix components being equal to the number Kf of matrix components.
  • step 430 for each candidate parameter set, the matrix M1 is factorized using a matrix factorization method.
  • a candidate parameter set of the matrix factorization method may include: a number Kf of matrix components for the matrix factorization and / or a subset Gf of a set of gene expression components used for representing the first reference transcriptomic profiles.
  • L be the number of the candidate parameter sets including each a combination of Kf and Gf to be tested (1 ⁇ f ⁇ L).
  • the matrix M1 will be factorized three times to get three projection matrices P1f (1 ⁇ f ⁇ 3) representing the corresponding projections of the N1 vectors on the Kf components.
  • projection matrix P1f are generated by varying the parameters such as the number of components Kf and / or the number (or set) of genes Gf selected among G genes.
  • each of the Kf component of the projection matrice P1f is compared to the tumor sensitivity measure to identify, for each projection matrix P1f, a component Kf m that is the most predictive of the measure of sensitivity associated with the first reference transcriptomic profiles corresponding to the tumor samples of the first batch B1 of tumor samples.
  • the purpose is here to identify one single component Kf m for each projection matrix that allows to predict for a transcriptomic profile the sensitivity degree of the represented tumor sample to a candidate therapeutic molecule.
  • This identification may be performed by evaluating a statistical association between the distribution of the projections of the N1 vectors on the Kf components and the measures of sensitivity of the tumor samples to a candidate therapeutic molecule associated with the N1 vectors.
  • a prediction function PFf is determined as a projection function P1f m on the most predictive component Kf m among all the components obtained for the L projection functions of the N1 vectors.
  • a projection function P1f m (or projection vector) on the Kf m component is determined: this projection function P1f m corresponds to the candidate prediction function PF f for the candidate parameter set f comprising Kf and Gf.
  • This identification may be performed by evaluating a statistical association between the distribution of the projections of the N1 vectors on the Kf components and the sensitivity of the tumor sample to a target therapeutic molecule associated with the N1 vectors.
  • the evaluation of the statistical association may be performed using ANOVA (Analysis of Variance), linear models, generalized linear models, Kruskal-Wallis test, Pearson and Spearman correlation.
  • constraint(s) can be applied to discard unfit components, such as an absence of correlation with other measures of tumor proliferation. This will result in a sub-selection of L' projection functions with L' ⁇ L.
  • a second matrix M2 of transcriptomic profiles is generated from second reference vectors V2i representing respectively the second reference of transcriptomic profiles of tumor samples of the second batch B2 of tumor samples.
  • the samples may be of human or animal origin.
  • step 450 like in step 350, a measure of sensitivity of the tumor to the candidate therapeutic molecule is obtained for each second reference transcriptomic profile.
  • Each projected vector P2f obtained for the selected component Kf m is compared to the N2 molecular target sensitivity measures.
  • step 470 an accuracy analysis is performed to select a prediction function SPF (selected projection function) among the candidate prediction functions (projection functions) P1f m : the selected projection function SPF is the projection function for which the predicted sensibility degrees obtained for the tumor samples of the second batch B2 and the measures of sensitivity obtained for the tumor samples of the second batch B2 of tumor samples are the most correlated.
  • the projection function P1f s is selected, wherein s is the value of f from 1 to L (or respectively L') for which the projected vector P2f is the most discriminant for the respectively N2 target therapeutic molecule sensitivity measures.
  • any supervised and semi supervised machine learning method may be used for implementing the steps 310-370 or 410-470 including the generation of L candidate prediction functions from first reference transcriptomic profiles and their respective measure of sensitivity to a therapeutic target molecule and the selection of one among the L candidate prediction functions using second reference transcriptomic profiles and their respective measure of sensitivity to the same therapeutic target molecule.
  • the previous method steps 410-430 may be performed a first time with a first large set of genes until the coefficient matrix Q1s is obtained. Then only a reduced set of genes may be used for the determination of the prediction function at steps 440-470. For example, the genes having the highest coefficients in absolute value in the coefficient matrix Q1s may be kept for the reduced set of genes. The method steps 410-470 may be performed again with this reduced set of genes to get a corresponding coefficient matrix Q1s and projection matrix P1s for the reduced set of genes. Thus, only a reduced set of genes may be used, thereby simplifying the computations and limiting the resources that are necessary both for the RNA analysis and the computation of the matrices, projection values, etc.
  • Figure 4 shows a flowchart of an example method for determining a sensitivity degree of a tumor sample of a patient to one or more candidate therapeutic molecules. While the steps are described in a sequential manner, the man skilled in the art will appreciate that some steps may be omitted, combined, performed in different order and / or in parallel. Figure shows a data processing diagram that illustrates the processing steps of the method of figure 4 .
  • step 505 a vector representative of a transcriptomic profile is obtained for the concerned tumor sample.
  • a sensitivity degree of a tumor sample of a patient to the concerned candidate therapeutic molecule is determined, using any suitable method described herein, for example using the method described by reference to figure 1 or 2 .
  • the selected candidate prediction function SPF (e.g. the candidate prediction function selected in step 370 or 470) is used as prediction function to generate a sensitivity degree of the tumor sample to the candidate therapeutic molecule.
  • a sensitivity degree to the candidate therapeutic molecule is obtained as the result of the prediction function applied to the vector representing the transcriptomic profile of the tumor sample of the patient.
  • the steps 510 and 520 may be repeated for at least one second candidate therapeutic molecule to obtain a sensitivity degree for each of at least one second candidate therapeutic molecule.
  • a sensitivity degree may be obtained for each of a plurality of candidate therapeutic molecules.
  • At least one candidate therapeutic molecule is selected on the basis of a comparison of the sensitivity degrees obtained each of a plurality of candidate therapeutic molecules.
  • the candidate therapeutic molecule suitable for this patient may be selected as a candidate therapeutic molecule having the highest sensitivity degree or one of the highest sensitivity degrees.
  • a method for determining a sensitivity signature predictive of the sensitivity of one or more pancreatic ductal adenocarcinoma (PDAC) samples to a target therapeutic molecule is also disclosed.
  • RNA signature predictive of the sensitivity degree.
  • This sensitivity signature will be referred to herein as the RNA signature.
  • the number of genes in the RNA signature may be lower than 100, for example lower than 50 genes or lower than 20 genes.
  • a sensitivity signature to the candidate therapeutic molecule or RNA signature for the candidate therapeutic molecule may be computed based on the candidate prediction function selected at step 370 and /or step 470.
  • the RNA signature may be computed as a distribution of prediction values generated by applying to each of the second reference vectors the selected candidate prediction function SPF obtained at step 370 and /or step 470.
  • the RNA sensitivity signature is computed as a distribution of projection values of the N2 vectors on the Ksm component of the P2s projection function.
  • the sensitivity degrees of the distribution may be scaled to be defined on a range of easily interpretable values, for instance between 0 and 1 to be expressed as percentage or normalized around zero with zero mean and unit standard deviation.
  • a correspondence between the sensitivity measures and a distribution of prediction values of vectors representing reference transcriptomic profiles belonging is taken into account for determining the orientation of the prediction value scale.
  • the orientation of the scale of the prediction values is determined on the basis of the location, on the scale of the prediction values, of the prediction values of one or more second reference vectors associated with the highest and / or lowest measures of sensitivity.
  • the scale of prediction values may be normalized to facilitate interpretation of the prediction values. For example, to get a normalized scale between -1 and +1 or between 0 and 1, each prediction value is normalized with respect to the mean value and / or lowest and highest values and / or standard deviation of the distribution.
  • the scale of prediction values may more generally be a defined by its upper and lower values such as [0, 100], [-100; +100], [0; 1] [-1, +1], etc.
  • the scaling function may be defined in different manner. For example, a normalized score nsc may computed from a non-normalized value sc, the mean m and standard deviation ⁇ of the distribution corresponding to the reference RNA sensitivity signature. In an example, the standard deviation ⁇ is multiplied by a real number w (scaling coefficient) that may be set between 1 and 3 (1 ⁇ w ⁇ 3).
  • the RNA sensitivity signature enables to represent the degree of sensitivity (or level of sensitivity) to a target molecular therapy associated with a transcriptomic profile using a single numerical value defined relatively to a scale of values.
  • a continuous scale or a scale of discrete values may be used.
  • the sensitivity degree to a target molecular therapy may be computed for any transcriptomic profile.
  • the gene expression is an RNA expression and the specific set of genes, as defined by the HUGO Gene Nomenclature Committee, includes part or all of the following genes: MIR205HG, C20orf197, IGFBP1, FAT2, NIPAL4, CIDEC, CSTA, TMEM37, PDE11A, PBX1, PVRL1, CTGF, GSTM4, TIMP2, RASSF4, ASTN2, C22orf23, MICALL1, TRAF3IP2, HAS3, KLHL41, ZCWPW2, TCOF1, RCC1, TRAP1, FBXO9, DAP, CD63, NOB1, RUVBL1
  • the RNA signature for gemcitabine is expressed using a specific set of genes including part or all of the genes of a reduced set of genes.
  • the identified reduced set of genes includes: MIR205HG, IGFBP1, FAT2, GAL, FGFR3, PARD3B, MAOB, CEL, PLA2G16, TMEM37, COL28A1, LRRC66, PDE11A, PBX1, C12orf54, DDN, NXPE1, MSI2, JUN, CHCHD6, C11orf84, MICU2, RCC1, FBXO9, NOB1, PRRC2B, CTNNB1, RPL5, RUVBL1, MANBAL.
  • the identified reduced set of genes includes: KRT5, GAL, C4BPB, CREB3L1, PLAC8, SLC40A1, MAOB, RNF144B, RNASE7, ADAD2, CAPN14, PLA2G16, LYPD6, PVRL3, SH2D3C, DDN, SRC, THSD7B, SNED1, FBL, MSI2, JUN, SOCS6, C11orf84, TRAK2, MICU2, LYAR, PRRC2B, RPL5, ALG5.
  • the identified reduced set of genes includes: EPS8L3, TNFRSF11B, VNN1, TSLP, ADD2, PLAC8, NDP, S100A4, RNASE7, ADAD2, CAPN14, FRY, LYPD6, CXCL3, CACNA2D4, PVRL3, SPRR2E, SLC7A5, SPTB, KRT2, SH2D3C, SRC, CDKN2C, TRAK2, PRKAB1, STX6, RABEPK, EIF2A, ALG5, ZFP64.
  • the RNA signature is expressed using a specific set of genes including part or all of the genes of a reduced set of genes.
  • the identified reduced set of genes includes: BVES, VCAM1, RNF150, EDAR, FXYD3, NFE4, PTPRZ1, C1orf106, SLC7A2, HEG1, RASEF, KLF17, CLCNKA, ATP8B1, HIPK2, NRP2, PDS5B, FBXL13, FERMT1, USP12, CDKN2C, PLBD2, MAGED1, ACP6, MGST2, TASP1, ITPA, ZFYVE1, PSMD2, RAC1.
  • the identified reduced set of genes includes: BVES, ZBED2, ANGPT1, LEMD1, PTPRZ1, C1orf106, SLC7A2, HEG1, RAB39B, LGI2, USP43, RASEF, KLF17, AKR1B1, HSPB7, EPS8L2, NRP2, MFGE8, ITGA6, STON2, IL17RE, DACT3, GRTP1, GABARAPL1, MGST1, FKBP1B, ANXA1, PLBD2, ACP6, USP5.
  • the identified reduced set of genes includes: CEACAM5, SRGN, LEMD1, KIF5C, RAB39B, SHISA3, CLDN7, LGI2, UNC5B, CYP3A5, HSPB7, CYP2W1, ANO9, N4BP2L1, C9orf170, ELOVL3, DIRAS1, ITGA6, SH3RF2, STON2, GRTP1, GABARAPL1, ANXA1, NET1, ETHE1, MAP3K3, ATN1, WBP11, WIPI1, USP5.
  • the identified reduced set of genes includes: AOC1, VCAM1, FXYD3, NFE4, ZNF385B, VSNL1, BRCA2, TMEM26, TPST1, NHLRC3, PROSER1, PDS5B, N4BP2L2, FERMT1, SMARCC1, STK24, PSD4, USP12, CPEB4, CDKN2C, ITSN1, PDE4DIP, MAGED1, GSTO2, CALU, P4HA3, CCDC77, ITPA, PSMD2, SLC41A1
  • the RNA signature is expressed using a specific set of genes including part or all of the genes of a reduced set of genes.
  • the identified reduced set of genes includes: TFF2, MLPH, LFNG, SLC16A3, GPC1, LDLRAD2, TFEB, HSPG2, IRS1, PC, NPAS2, METRNL, CHPF, HPCAL1, RGS10, SLC38A1, POU2F1, PRADC1, EIF4B, NPM1, RPAP3, RPS15A, IPO7, EIF2A, TAF1D, TAF1B, PFDN4, SNHG1, NACA, TFAM.
  • the identified reduced set of genes includes: TNS4, LFNG, SLC16A3, DUSP4, GPC1, AHR, TFEB, ITGB5, HPCAL1, RGS10, EPHA2, EPHX1, POU2F1, RABAC1, STOML1, STAG2, CCNJ, DDX21, BCL2L1, CRELD2, IQCB1, ARID2, IARS, RPS24, RPS15A, DKC1, CSNK2A2, PFDN4, TFAM, GAR1.
  • the identified reduced set of genes includes: PSCA, ALOX5, HPGD, TIMP1, TMEM105, DUSP4, SMPD1, LONRF1, EPHX1, S100A6, XPOT, RABAC1, STOML1, STAG2, CCNJ, BCL2L1, ARID2, MYEOV2, THG1L, PIP5K1C, LETMD1, DKC1, CUL1, ST13, U2SURP, GAR1, RPL22, OST4, RPL6, RNF41.
  • the identified reduced set of genes includes: VSTM2L, PCDH7, MLPH, FOXQ1, ZDHHC22, SH3BP4, LDLRAD2, IRS1, PC, NPAS2, ABCA7, ITM2C, PLXNB2, RHOB, INF2, SLC38A1, PGAP3, NAP1L1, GRPEL2, EIF4B, LTA4H, RPAP3, LRPPRC, EIF2A, TAF1B, RPL5, HNRNPA1, EIF3M, SUPV3L1, EIF2S2.
  • the RNA signature is expressed using a specific set of genes including part or all of the genes of a reduced set of genes.
  • the identified reduced set of genes includes: GPR1, SELPLG, CAPN14, TIMP1, NRARP, EPB41L4A-AS2, FZD1, IRX5, DDN, PON2, SRC, KAZN, C3orf18, AMDHD1, MTL5, PROX2, TMEM159, CPNE2, RRP36, ELK4, NSF, DCAF10, CTBP1-AS2, RPS7, CLNS1A, MSL2, RPL11, TM9SF1, RNF25, DPY30.
  • the identified reduced set of genes includes: FAM65B, SSTR1, PLAU, CAPN14, SLC2A5, PBX1, NRARP, IL2RB, FAM111B, GCSAM, PON2, AMDHD1, TMEM159, FAM60A, RRP36, PABPC1, ACAA1, SLC25A36, SORD, RPS3, MYEOV2, FOXK1, NSF, OSBPL2, MSL2, IP6K1, ARPC4, TM9SF1, ATP6AP1, DPY30.
  • the identified reduced set of genes includes: FAM65B, CNR1, IGFL2, PNMT, VSTM2L, SLC13A3, SLC2A5, GUCY1B2, HOXD8, GLIPR2, CHN2, ABLIM3, GBX1, GCSAM, E2F2, FRMD4A, FAM60A, ACAA1, SORD, FOXK1, GMPPB, RPS27A, RPL24, OSBPL2, RPL31, IP6K1, EIF2S2, NDUFB1, ATP6AP1, CPSF3.
  • the identified reduced set of genes includes: DCN, RBP4, GPR1, MAOB, AIF1L, TLE6, EPB41L4A-AS2, FSTL3, IRX5, FLVCR2, RPS6KA5, DDN, CITED2, SRC, MTL5, CYB561D2, MTHFD2, SIAH2, DCAF10, CTBP1-AS2, GYG1, ACP2, EIF2A, RPS7, BZW2, CCT4, RNF25, EMC3, COX7A2L, LCMT1
  • the RNA signature is expressed using a specific set of genes including part or all of the genes of a reduced set of genes.
  • the identified reduced set of genes includes: SLC13A3, NOS3, RAMP2, ASNS, OPTC, SMAGP, FASN, RGS10, PERI, FRMD4A, MLLT1, GFPT1, ACVR2B, RPTOR, LRPAP1, MYEOV2, BCOR, P4HB, DCAF10, DCLRE1C, VPS18, NSMCE4A, NT5C3A, ANKRD54, GDI2, BCS1L, RNF40, EMC3, PI4KB, SMIM12.
  • the identified reduced set of genes includes: FN1, FAM65B, CPE, COLCA1, SLC13A3, RAMP2, ADCY4, SMPD1, C17orf78, LPXN, STX3, MLLT1, LRPAP1, MYEOV2, BCOR, SLC39A3, LMAN2, DCAF10, DHX32, IL17RA, FNBP4, MGEA5, CSNK1D, NT5C3A, OS9, GDI2, BZW2, EIF2S2, L3MBTL2, EMC3.
  • the identified reduced set of genes includes: ROS1, FN1, CPE, FZD1, GSDMB, AREG, SMPD1, C17orf78, INHBC, PRELID2, TRAF5, LPXN, TTC39B, RPIA, CEBPG, LTA4H, PIP5K1C, GARS, R3HCC1, SLC39A3, LMAN2, TAF1D, ZNF142, DHX32, LRFN3, MUL1, MGEA5, MRPS7, EIF2S2, RNF25.
  • the identified reduced set of genes includes: LOXL1, BEND7, NOS3, EPB41L4A-AS2, ASNS, OPTC, OSTN, GPT2, RGS10, FRMD4A, SLC35D2, TMEM104, SLC4A5, CRELD2, RPTOR, PPP5C, CLPTM1, E2F5, P4HB, MTHFD2, MKL1, TIAL1, ALG12, ANKRD54, BCS1L, TM9SF1, RBM17, IPO13, SLC25A44, SMIM12.
  • Annexed figures 7a and 7b show the transcriptomic signature of sensitivity to gemcitabine in 38 cell lines.
  • Annexed figures 8a and 8b illustrate the transcriptomic signature of sensitivity to gemcitabine in 12 patient derived xenografts.
  • the 12 PDX are ordered by the score given by the gemcitabine signature on the transcriptomic profiles of the PDX.
  • the figure also shows the sensitivity of PDX to gemcitabine (ordered by the signature) using the normalized volume difference between the gemcitabine-treated and control PDX and the associated growth rates.
  • Annexed figures 9a and 9b illustrate the transcriptomic signature of sensitivity to irinotecan in 12 patient derived xenografts.
  • the 12 PDX are ordered by the score given by the irinotecan signature on the transcriptomic profiles of the PDX.
  • the figure also shows the sensitivity of PDX to irinotecan (ordered by the signature) using the normalized volume difference between the irinotecan-treated and control PDX and the associated growth rates.
  • Example 5 Additional example of determining the chemosensitivity of PDAC by Transcriptomics
  • Each cell line was treated with gemcitabine, irinotecan, oxaliplatin, docetaxel or 5FU with varying dosage from 0.001 ⁇ M to 1000 ⁇ M.
  • Cell survival was quantified using the Cell Titer-Glo assay (Promega Corporation) 3 days after adding chemo to the culture media.
  • N most differential genes were selected using a mean-variance relationship-based precision weight for a moderate t-test.
  • the sets of N selected genes are hereafter referred to as the
  • Chemosensitivity transcriptomic signatures are Chemosensitivity transcriptomic signatures.
  • the set of most predictive genes for each of the 18 comparisons were selected based on the combination of criteria: the accuracy of classification into resistant or sensitive in a 10-times repeated 3-fold cross-validation using the gene expression of the potential gene set to evaluate, the spearman correlation between the probability of being classified as sensitive and the actual metric for all cell lines (not only those considered as resistant or sensitive), and the R2 of a regression model using all the cell lines and the gene expression values of the potential gene set to evaluate in a 10-times repeated 3-fold cross-validation setting.
  • Gene sets were ranked for these three criteria in each of the 18 drug-metric pair comparison and the gene set with the best mean rank was selected. This resulted in 18 sets of genes for each metric-drug pair, which are hereafter referred to as the chemosensitivity transcriptomic signatures.
  • the chemosensitivity transcriptomic signatures are used to derive a supervised binary classifier from the transcriptomic profiles of the 3 sensitive and 3 resistant cell lines.
  • the supervised classifier is trained on the 6 cell lines using the gene expression levels of the associated chemosensitivity transcriptomic signatures as the explanatory variables X used to predict the dependent binary variable Y encoded as 0 for resistant cell lines and 1 for sensitive cell lines.
  • Any binary classification model can be used for this task, including but not limited to: Support Vector Machine, Random Forest, Linear or Quadratric Discriminant Analysis, Generalized linear models, Penalized linear models, k-nearest neighbor, nearest centroid, etc.
  • Regression methods can also be applied directly on the growth rate normalized sensitivity metrics.
  • the explanatory variables X are composed of the gene expression values of the chemosensitivity signatures in all cell lines (instead of the 3 sensitive vs 3 resistant) and the dependent variable Y is the appropriate growth rate normalized sensitivity metric.
  • any supervised or semi-supervised regression model can be used, including but not limited to: Support Vector Regression, Random Forest, Generalized linear models, Penalized linear models, matrix factorization approaches, etc.
  • This step results in a task-specific prediction algorithm obtain from the training of a supervised binary classification, a supervised regression or a semi-supervised model.
  • the predicting model Trained on the cell lines gene expression values, the model can be applied in a new sample for which the necessary gene expression values X are known, the model will output a value ⁇ , either a probability of being sensitive for binary classification models or a prediction of the growth rate normalized sensitivity metric for supervised regression and semi-supervised models.
  • This value ⁇ informs directly on the sensitivity and the type of sensitivity (potency, efficacy or global) of a sample to the chemotherapeutic agent associated to the signature used.
  • This procedure can be applied to any PDAC gene expression profiles providing an ⁇ value for each of the chemotherapeutic sensitivity signatures.

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Cited By (2)

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Publication number Priority date Publication date Assignee Title
CN113688908A (zh) * 2021-08-25 2021-11-23 江南大学 基于在线ε型孪生支持向量回归机的蓝牙信号室内传播模型校正方法
CN115966316A (zh) * 2023-02-10 2023-04-14 北京大学 肿瘤药物敏感性预测方法、系统、设备及存储介质

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